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From the book 1.4 Languages and Semantic Model

1.4 Languages and Semantic Model

At the beginning of this example, I talked about building a model for a state machine. The presence of such a model, and its relationship with a DSL, are vitally important concerns. In this example, the role of the DSL is to populate the state machine model. So, when I’m parsing the custom syntax version and come across:

 doorClosed D1CL 

I would create a new event object (new Event("doorClosed", "D1CL")) and keep it to one side (in a Symbol Table (165)) so that when I see doorClosed => active I could include it in the transition (using addTransition). The model is the engine that provides the behavior of the state machine. Indeed you can say that most of the power of this design comes from having this model. All the DSL does is provide a readable way of populating that model—that is the difference from the command-query API I started with.

From the DSL’s point of view, I refer to this model as the Semantic Model (159). When people discuss a programming language, you often hear them talk about syntax and semantics. The syntax captures the legal expressions of the program—everything that in the custom-syntax DSL is captured by the grammar. The semantics of a program is what it means—that is, what it does when it executes. In this case, it is the model that defines the semantics. If you’re used to using Domain Models [Fowler PoEAA], for the moment you can think of a Semantic Model as very close to the same thing.

Figure 1.4 Parsing a DSL populates a Semantic Model (159).

(Take a look at Semantic Model (159) for the differences between Semantic Model and Domain Model, as well as the differences between a Semantic Model and an abstract syntax tree.)

One opinion I’ve formed is that the Semantic Model is a vital part of a well-designed DSL. In the wild you’ll find some DSLs use a Semantic Model and some do not, but I’m very much of the opinion that you should almost always use a Semantic Model. (I find it almost impossible to say some words, such as “always,” without a qualifying “almost.” I can almost never find a rule that’s universally applicable.)

I advocate a Semantic Model because it provides a clear separation of concerns between parsing a language and the resulting semantics. I can reason about how the state machine works, and carry out enhancement and debugging of the state machine without worrying about the language issues. I can run tests on the state machine model by populating it with a command-query interface. I can evolve the state machine model and the DSL independently, building new features into the model before figuring out how to expose them through the language. Perhaps the most important point is that I can test the model independently of futzing around with the language. Indeed, all the examples of a DSL shown above were built on top of the same Semantic Model and created exactly the same configuration of objects in that model.

In this example, the Semantic Model is an object model. A Semantic Model can also take other forms. It can be a pure data structure with all behavior in separate functions. I would still refer to it as a Semantic Model, because the data structure captures the particular meaning of the DSL script in the context of those functions.

Looking at it from this point of view, the DSL merely acts as a mechanism for expressing how the model is configured. Much of the benefits of using this approach comes from the model rather than the DSLs. The fact that I can easily configure a new state machine for a customer is a property of the model, not the DSL. The fact that I can make a change to a controller at runtime, without compiling, is a feature of the model, not the DSL. The fact I’m reusing code across multiple installations of controllers is a property of the model, not the DSL. Hence the DSL is merely a thin facade over the model.

A model provides many benefits without any DSLs present. As a result, we use them all the time. We use libraries and frameworks to wisely avoid work. In our own software, we construct our models, building up abstractions that allow us to program faster. Good models, whether published as libraries or frameworks or just serving our own code, can work just fine without any DSL in sight.

However, a DSL can enhance the capabilities of a model. The right DSL makes it easier to understand what a particular state machine does. Some DSLs allow you to configure the model at runtime. DSLs are thus a useful adjunct to some models.

The benefits of a DSL are particularly relevant for a state machine, which is particular kind of model whose population effectively acts as the program for the system. If we want to change the behavior of a state machine, we do it by altering the objects in its model and their interrelationships. This style of model is often referred to as an Adaptive Model (487). The result is a system that blurs the distinction between code and data, because in order to understand the behavior of the state machine you can’t just look at the code; you also have to look at the way object instances are wired together. Of course this is always true to some extent, as any program gives different results with different data, but there is a greater difference here because the presence of the state objects alters the behavior of the system to a significantly greater degree.

Adaptive Models can be very powerful, but they are also often difficult to use because people can’t see any code that defines the particular behavior. A DSL is valuable because it provides an explicit way to represent that code in a form that gives people the sensation of programming the state machine.

The aspect of a state machine that makes it such a good fit for an Adaptive Model is that it is an alternative computational model. Our regular programming languages provide a standard way of thinking about programming a machine, and it works well in many situations. But sometimes we need a different approach, such as State Machine (527), Production Rule System (513), or Dependency Network (505). Using an Adaptive Model is a good way to provide an alternative computational model, and a DSL is good way to make it easier to program that model. Later in the book, I describe a few alternative computational models (“Alternative Computational Models”) to give you a feel of what they are like and how you might implement them. You may often hear people refer to DSLs used in this way as declarative programming.

In discussing this example I used a process where the model was built first, and then a DSL was layered over it to help manipulate it. I described it that way because I think that’s an easy way to understand how DSLs fit into software development. Although the model-first case is common, it isn’t the only one. In a different scenario, you would talk with the domain experts and posit that the state machine approach is something they understand. You then work with them to create a DSL that they can understand. In this case, you build the DSL and model simultaneously.

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